Rethinking deep learning in bioimaging through a data centric lens

Jiajun Cao; Jan Wenzel; Shanghang Zhang; Josephine Lampe; Hongxiao Wang; Jiachen Yao; Zhicheng Zhang; Shuo Zhao; Yu Zhou; Chao Chen; Markus Schwaninger; Jufeng Yang; Danny Z Chen; Jianxu Chen

Rethinking deep learning in bioimaging through a data centric lens

Jiajun Cao, Jan Wenzel, Shanghang Zhang, Josephine Lampe, Hongxiao Wang, Jiachen Yao, Zhicheng Zhang, Shuo Zhao, Yu Zhou, Chao Chen, Markus Schwaninger, Jufeng Yang, Danny Z Chen, Jianxu Chen

Institut für Experimentelle und Klinische Pharmakologie und Toxikologie

3 Zitate (Scopus)

Abstract

Deep learning has become essential in bioimaging for tasks. By examining data-centric strategies in general AI and revisiting existing deep learning methods in bioimaging, we describe a prototypical “BioData-Centric AI” framework. For AI users in bioimaging, this framework promotes a more practical approach beyond simply annotating large datasets or relying on a universal model. For method developers, it highlights key research directions to enhance AI toolboxes for the bioimaging community.

Originalsprache	Englisch
Aufsatznummer	29
Zeitschrift	NPJ Imaging
Jahrgang	3
Ausgabenummer	1
Seiten (von - bis)	29
ISSN	2948-197X
Publikationsstatus	Veröffentlicht - 26.06.2025

Fördermittel

Y.Z., S.Z., and J.C. were supported by the Federal Ministry of Education and Research (BMBF) in Germany under the funding reference 161L0272, and also supported by the Ministry of Culture and Science (MKW) of the State of North Rhine-Westphalia. H.W. was supported by Beijing Natural Science Foundation Youth Fund (Grant No. 4254093). J.C. and S.Z. were supported by the National Science and Technology Major Project (No. 2022ZD0117800). J.W. was supported by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) (reference WE 6456/1-1). M.S. was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 810331).

Träger	Trägernummer
Ministerium für Kultur und Wissenschaft des Landes Nordrhein-Westfalen
European Research Council
Horizon 2020 Framework Programme	810331
Deutsche Forschungsgemeinschaft	WE 6456/1-1
National Science and Technology Major Project	2022ZD0117800
Bundesministerium für Forschung, Technologie und Raumfahrt	161L0272
Natural Science Foundation of Beijing Municipality	4254093

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AU - Yao, Jiachen

AU - Zhang, Zhicheng

AU - Zhao, Shuo

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AB - Deep learning has become essential in bioimaging for tasks. By examining data-centric strategies in general AI and revisiting existing deep learning methods in bioimaging, we describe a prototypical “BioData-Centric AI” framework. For AI users in bioimaging, this framework promotes a more practical approach beyond simply annotating large datasets or relying on a universal model. For method developers, it highlights key research directions to enhance AI toolboxes for the bioimaging community.

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Rethinking deep learning in bioimaging through a data centric lens

Abstract

Fördermittel

UN SDGs

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